In recent years, the flexible job shop dynamic scheduling problem (FJDSP) has received considerable attention; however, FJDSP with transportation resource constraint is seldom investigated. In this study, FJDSP with transportation resource constraint is considered and an improved non-dominated sorting genetic algorithm-III (NSGA-III) algorithm (DQNSGA) integrated with reinforcement learning (RL) is proposed. In DQNSGA, an initialization method based on heuristic rules and an insertional greedy decoding approach are designed, and a double-Q Learning with an improved ε -greedy strategy is used to adaptively adjust the key parameters of NSGA-III. An improved elite selection strategy is also applied. Through extensive experiments and practical case studies, this algorithm has been compared with three other well-known algorithms. The results demonstrate that DQNSGA exhibits significant effectiveness and superiority in all tests. The research presented in this paper enables effective adjustments of production plans in response to dynamic events, which is of critical importance for production management in the manufacturing industry.

Industrial Internet enables distributed manufacturing enterprises to efficiently and promptly respond to the requirements of stakeholders using a manufacturing service collaboration chain (MSCC) composed of networked enterprises. However, various dynamic uncertainties may interrupt the MSCC, such as device malfunctions, urgent order insertions, and dynamic logistics, resulting in inexactitude practical applications. In this article, we propose a novel reliability-based dynamic manufacturing service collaboration optimization (R-DMSCO) model for uncertain manufacturing collaboration procedures on industrial Internet. The R-DMSCO model reformulates the MSCC reliability in the form of an expectation–standard deviation of uncertain job completion time described by discrete scenarios pertaining to the uncertain perturbation of processing time and logistics time. Subsequently, an enhanced multiobjective artificial bee colony (EMOABC) algorithm that embeds four improvements is intended to address the manufacturing service collaboration optimization (MSCO) problem. The experimental results demonstrate that EMOABC outperforms other typical multiobjective algorithms for MSCO problems. Additionally, the R-DMSCO model can cope with dynamic uncertainties with better robustness and stability than two other effective strategies for dynamic manufacturing service collaboration.